Cone beam CT scanners are used to produce three-dimensional X-ray images of anatomy and other objects. Cone beam CT systems capture volume data sets by using a high frame rate flat panel digital radiography (DR) detector and an x-ray source, typically affixed to a gantry that revolves about the object to be imaged, directing, from various points along its orbit around the subject, a divergent cone beam of x-rays toward the subject. The CBCT system captures projection images throughout the source-detector orbit, for example, with one 2-D projection image at every degree of rotation. The projections are then reconstructed into a 3D volume image using various techniques.
One concern with increased use of CT and CBCT scanners relates to radiation exposure. One strategy that can be employed to limit exposure is to narrow the field of view of the exposure so that only an object of interest is exposed, sparing surrounding tissue from radiation exposure. However, 3-D object imaging with a cone beam scanner has undesirable consequences. Because only the 3-D object is imaged with a narrow field-of-view (FOV) the X-ray images that are captured by the cone beam scanner's detector are incomplete. These incomplete X-ray images are commonly referred to as truncated projections because they are projections of the X-ray source through only part of the larger volume that contains the object. Projections are referred to as “width truncated” because the X-rays that are incident on the left, right, or both edges of the detector, that are in the direction of the axis of rotation of the scanner, pass through the object.
There are a number of undesirable consequences of capturing truncated projections of a 3-D object with a cone bean scanner. Some of these relate to image reconstruction. A filtered back-projection method is often used to reconstruct a three-dimensional image from two-dimensional X-ray projections that are captured by the detector. In employing this method, however, it is assumed that the projections are not truncated, but that the full width of the object is fully imaged at all projection angles. When this arrangement is compromised, artifacts can be introduced into the reconstructed image. Furthermore, the X-ray attenuation coefficients of the reconstruction are incorrect.
Scatter presents another problem that is accentuated for truncated projections. When an X-ray source passes through an object, some of the photons are scattered so that their path does not lie on a straight line from the X-ray source to a pixel of the detector. Scattered photons may eventually reach the detector and result in a signal. Such scattered X-ray radiation can significantly reduce the contrast of the reconstructed image, reducing its usefulness for diagnostic purposes. Methods have been developed to remove the scatter component of the detected signal. However, if the scatter component is inaccurately estimated, only partial scatter removal is possible and the contrast of the image is degraded. The process of scatter removal can introduce unwanted streaks into the reconstructed image. Among methods developed to accurately calculate the scatter signal are Monte Carlo scatter calculations; however these methods generally require complete knowledge of the scanned object which is missing when only a 3-D object can be fully reconstructed and the image of the larger volume of surrounding tissue is truncated.
Beam hardening is also a factor. When a polychromatic X-ray source propagates through an object, its spectrum changes due to the energy dependence of the X-ray attenuation coefficient of materials in the object. In general, the attenuation of X-rays increases as its energy decreases. Hence, as polychromatic X-rays propagate through an object the energy distribution shifts and becomes higher or “harder.” This X-ray beam hardening effect results in artifacts in the reconstructed image including “cupping” and dark bands between highly attenuating material.
Furthermore, energy resolving detectors have been developed, including photo counting detectors, that enable the material composition of a scanned object to be determined. This feature, however, requires knowledge of the X-ray energy spectrum throughout the scanned volume. Unfortunately, in 3-D object imaging when the whole volume cannot be fully reconstructed, the X-ray spectrum within the 3-D object is unknown. This, in turn, impedes the prevention and correction of beam hardening artifacts and the determination of the material constituents of the 3-D object.
Movement of the patient or imaging apparatus can also be a problem. When an object is scanned by a cone beam system, the object may move, especially if the scan is of a live patient for medical or dental imaging. In addition, the X-ray source and detector may not follow an ideal path during the scan due to flex in the scanner or to imperfections in the mechanical system. Using conventional motion detection techniques, knowledge of the contour of the scanned 3-D object can be used to determine the location of the scanned object relative to the scanner. This location information can then be used in the reconstruction process to remove motion artifacts from the reconstruction. Because contrast and image sharpness can be compromised, this process risks rendering the reconstruction unusable for diagnostic purposes. Moreover, when only a 3-D object is imaged, the contours of the object may not be clearly visible.
Furthermore, it is desirable to use iterative reconstructions methods in addition to, or in place of, filtered back-projection methods for a number of reasons. For example, reconstructions that are generated using algebraic reconstruction do not exhibit short scan artifacts which are found in filtered back-projection reconstructions when the range of source angles is less than 360 degrees. Statistical reconstruction methods generally produce superior quality reconstructions under low X-ray exposure conditions when photon Poisson noise and detector noise are significant. A problem arises when applying these reconstruction methods in 3-D object imaging when the whole object cannot be fully reconstructed; these methods require a forward projection step which requires knowledge of the whole object.
Truncation complicates the reconstruction task with respect to factors such as those noted. Thus, there is a need for improved truncation processing for CBCT images that allows improved image quality along with the advantages of reduced exposure.